Through the use of Artificial Intelligence and its wide suite of applications, a number of organizations have been able to reinvent themselves and provide more to their customers. One example of this is the way in which the IT industry operates with Machine Learning. With the use of Machine Learning, programs are able to learn all on their own. Without any sort of supervision or interaction, these programs can analyze and identify problems, solutions and trends for organizations all around the world. The possibilities are seemingly endless. Should this be the first time you are hearing about this, or you aren’t entirely sure what Machine Learning actually is, you may wish to check out this article https://www.fortinet.com/resources/cyberglossary/what-is-machine-learning on Fortinet, which is full of information so, hopefully, you will be able to become a little more familiar with what Machine Learning is.
How do these sorts of advancements affect the everyday user, though? Well, there are a number of recognizable ways in which Machine Learning and its counterparts influence our everyday lives. Let’s start with some of the most common social networks today. Has Instagram or Facebook ever recommended that you follow or friend a particular user? Was that user someone you knew closely? Most likely, as these recommendations are predicated on elements such as similar follows, similar followers, etc. The same way these networks suggest contacts, is the same way that Amazon suggests products to you. These are just some of the simple ways Machine Learning influences these applications. Much more complex services are built upon these Machine learning capabilities. Think about the automated fraud detection offered by your bank. How about how your phone’s suggested text patterns that can finish your texts for you? Or the most advanced translation services around the world?
All of the tools listed above were accomplished as a result of python-powered machine learning. How can these things be accomplished, though? First and foremost, companies must collect a massive amount of data on their customers. This data then needs to be assessed. After proper assessment, programmers may have to do the necessary coding or use necessary software tools like KNIME (https://www.knime.com/knime-analytics-platform) to analyze this data and provide sound insights that can eventually evolve into fully fleshed out business strategies. The programming language of choice for these challenges is Python.
Python has become the premier programming language for these types of highly data-sensitive operations. Artificial Intelligence, Data Science, Financial Services, you name it. Anything that requires working alongside large and consistently changing data sets is the ideal situation for utilizing Python. Due to its straightforward syntax, community support including extensive libraries of pre-written code able to be utilized for free, in addition to its flexibility to work in connection with different programming languages make it the best choice for these projects.
For additional information regarding Python and how it’s used in Machine Learning and Data Science applications, be sure to review the accompanying infographic coupled alongside this post. Within it, you’ll find a number of additional ways in which Python is utilized in this space, and the ways in which businesses can incorporate it themselves.
Author Bio: Anne Fernandez – Anne joined Accelebrate in January 2010 to manage trainers, write content for the website, implement SEO, and manage Accelebrate’s Python Training Courses. In addition, she helps to recruit trainers and works on various projects to promote the business.
